In the last few years, biomarkers have been used in basic and clinical research and their presence in primary endpoints in clinical trial is easily accepted. There are specific biomarkers that have been well characterized and repeatedly shown to correctly predict relevant clinical outcomes across a variety of treatments and populations but in many cases, the validity of biomarkers should always be evaluated.
My aim in this article is to discuss the utility of biomarkers in different processes such as drug development.
A biomarker is a biological characteristic that is objectively measured and evaluated as an indicator of normal biological or pathological processes or a response to therapeutic intervention; examples include patterns of gene expression, levels of a particular protein in body fluids, or changes in electrical activity in the brain.
In the spring of 2015 the FDA-NIH Joint Leadership Council worked together to harmonize terms used in translational science and medical product development as a priority need, with a focus on terms related to study endpoints and biomarkers. Their aim was to improve communication, align expectations, and improve scientific understanding. The two agencies developed the BEST (Biomarkers, EndpointS, and other Tools) Resource.
Biomarkers can be included in seven categories:
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Susceptibility/risk biomarker
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Diagnostic biomarker
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Monitoring biomarker
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Prognostic biomarker
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Predictive biomarker
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Pharmacodynamic/response biomarker
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Safety biomarker
Clinical endpoints or surrogate endpoints?
As Dr. Timbu and Dr. Tavel said: “Biomarkers play a critical role in improving the drug development process as well as in the larger biomedical research enterprise. Understanding the relationship between measurable biological processes and clinical outcomes is vital to expanding our arsenal of treatments for all diseases, and for deepening our understanding of normal, healthy physiology.”
For around 40 years, clinicians and researchers have been discussing the importance of using biomarkers as surrogate outcomes in large trials of major diseases, such as cancer.
To evaluate a biomarker, researchers and clinicians must completely understand the normal physiology of a biological process, the pathophysiology of that process in the disease state, and effects of a pharmacological intervention on these processes.
Unfortunately, these processes are hard to be figured out completely and biomarkers as surrogate endpoints need to be always reevaluated. Besides, studies using biomarkers should always have as final measures clinical outcomes correlation success, at least for retrospective analysis of biomarker. A regular reevaluation of the relationship between surrogate endpoints and true clinical endpoints has to be done otherwise the drug might either harm the patient or can be inefficient.
In 2012, in Oxford Global’s 7th Annual Biomarkers Congress, held in Manchester, England, researchers and clinicians focused on all types of biomarkers and their application (for example, prognostic and diagnostic markers, predictive markers, patient selection strategies in drug development, point-of-care solutions, pharmacodynamics and target engagement biomarkers as well as safety-related approaches). However, two specific fields were important: oncology and inflammatory diseases, This is because they provided best case studies for biomarker discovery and clinical utility.
Since then, emerging biomarkers in these therapeutic areas have increased.
How are biomarkers emerging?
I will expand on only few examples of the use of biomarkers in personalized cancer immunotherapy, in diagnostics and therapeutics and prognostic:
Biomarkers in personalized cancer immunotherapy
Tumor immune biology is a complex interaction of many immunosuppressive and immune stimulatory components involve in connected pathways that define the inflammatory state of the tumor microenvironment (TME).
Treatment induced can only be done when all key components are examined at a molecular and cellular level. By evaluating this complex interaction based on treatment outcomes in cancer, researchers could identify novel cell types that drive or contribute to the efficacy and/or resistance to therapy and biomarkers that can predict clinical outcome or drive mechanisms of rejection.
For this reason, the application of a potential biomarker in a clinical setting requires several steps of validation such as computational technology and bioinformatics, high-throughput biotechnology, gene expression arrays, mass spectrometry and fluorescence microscopes, plus a large and well-organized consortia and networks.
Biomarkers in diagnostics
The use of cytogenetic and molecular genetic biomarkers in clinical medicine helps to early detect and diagnose chromosome and single gene disorder.
Since 1956, biomarkers used in the chromosome analysis were developed and led to detect that many diseases were due to an abnormality in chromosome number (such as Turner syndrome, Down syndrome and Patau syndrome). Furthermore, there is a group of chromosome disorders that could be detected by biomarker copy number variation (CNV).
Biomarkers studies have helped people with either early intervention, or decision-making in marriages and pregnancies or even for and early disease management and treatment.
Unfortunately, it is not as easy sometimes as it can be since genetic analysis for early disease detection can be more complicated because not all genetic disorders are single gene responsible or chromosomal disorders.
Biomarkers in therapeutics and prognostics
As I have explained before, many genetic diseases are not as easy to manage as they first appear. Human geneticists encounter vast genetic heterogeneity in human disease that may lead to phenotype variability. The same disease may show different clinical manifestations or may be caused by different genetic mechanisms and/or may respond differently to the same treatment.
For example, the biomarker Philadelphia chromosome t(9;22) translocation is found in 95 percent of cases of chronic myeloid leukaemia (CML), which is caused by a chromosomal rearrangement that creates a fusion between two normal proteins producing one abnormal protein – BCR-ABL – that promotes a fast increase in the number of white blood cells and in some cases of acute lymphoblastic leukemia.
Due to this biomarker, imatinib has been developed to bind specifically to BCR-ABL and inhibits its action. Therefore, many studies have shown a highly improved response rates and lower toxicity for CML patients receiving imatinib compared with patients undergoing standard chemotherapy (Druker BJ, PMID: 12563611). However, over 90 percent of patients receiving imatinib respond positively to initial treatment and many experience complete remission.
Present and future
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To sum up, biomarker validation and approval is still a challenging field. It is difficult to repeat a study due to ethical and financial reasons therefore, minimum tolerance mistake is accepted.
Today, biomarkers are used in disease prevention, diagnosis, treatment, prognosis and drug development.
The discovery of biomarkers in recent years has increased rapidly thanks to the omics technologies in translational research and clinical medicine such as genomics, transcriptomics, proteomics, metabolomics and other high-throughput technologies. This includes the likes of targeted gene sequencing panels, Whole Exome Sequencing and Whole Genome Sequencing. They have improved the exactness for diagnosis/treatment and advanced personalized medicine.
In clinical medicine, molecular biomarkers such as cancer biomarkers are very challenging because in order to identify highly prevalent targets that constitute key master promoters of oncogenesis in specific tumors is very laborious and if a potential target is identified here comes the difficult part in order to discover new agents capable of improving normal cells functions by interacting with the target. A major barrier is that tumor cells might develop drug resistance.
For future developments, multiple biomarkers either from the same profile or from different profiles for instance DNA, mRNA, microRNA or protein and gene expression will be more often applied in clinical tests and more specifically in common diseases. So even though there are still many challenges to overcome, biomarkers are the key to drug development in the age of personalized medicine.
References
• Kyle Strimbu and Jorge A. Tavel, PMID 20978388
• Silver Spring (MD): Food and Drug Administration (US); Bethesda (MD): National Institutes of Health (US); 2016- Last Update: September 25, 2017
• Ellenberg SS, Hamilton JM, PMID:2727464) and heart disease (Wittes J, Lakatos E, PRobstfield J. PMID:2727465
• Tracy M, PMID: 23170309
• Jianda Yuan et al., PMID: 26788324
• Chen XH1, Huang S, Kerr D, PMID: 22997869
• Scherf U. et al., PMID: 20515286
• Omuro AM et al., PMID: 17620423